Recently, methods of performing object identification and the like using machine learning are being studied. As one of the machine learning methods, deep learning, which uses a neural network with several hidden layers between an input layer and an output layer, shows high recognizing performance.
And, the neural network using deep learning generally learns through backpropagation using losses.
In order to perform learning of such a deep learning network, training data in which tags, i.e., labels, are added to individual data points by labelers are needed. Preparing this training data (i.e., classifying the data correctly) can be very labour-intensive, expensive and inconvenient, especially if a large amount of training data is to be used and if the quality of the data pre-preparation is not consistently high. Conventional interactive labeling can be computationally expensive and fail to deliver good results.
Therefore, recently, auto-labeling which adds tags, i.e., labels, to training images using a deep learning-based auto labeling device is performed, and inspectors examine auto-labeled training images to correct the tags or the labels.
Accuracies of such conventional auto-labeling devices are being improved by re-learning.
However, there is a limit to the accuracies that can be improved by the re-learning of the auto-labeling devices, and higher accuracies require repetitive re-learning.
Also, the re-learning capable of improving the accuracies of the auto-labeling devices requires a huge amount of time, thus it takes an extended period of time to achieve useful accuracies of the auto-labeling devices.